The saphenous vein harvest procedure affects the arteriovenous system and postoperative wound healing in patients following coronary aortic bypass surgery
Bibliographic record
Abstract
Introduction One of the parts of the coronary artery bypass grafting (CABG) or off-pump coronary artery bypass grafting (OPCAB) process is the collection of vascular material, which is then employed as a coronary aortic bypass due to the large number of coronary vessels necessitating an aorto-coronary bypass. An invasive surgical operation called saphenous vein harvest, also known as the great saphenous vein (GSV), has the potential to cause surgical site infection (SSI). There are currently 2 methods for harvesting GSV: the conventional method open vein harvest (OVH) and the endoscopic, minimally invasive method endoscopic vein harvest (EVH). The clinical issue is whether the GSV harvest approach can influence the patient’s lower limb arteriovenous systems and help to lessen postoperative problems. Aim of the research To analyse the healing of a surgical incision on the lower limb and the effect of GSV harvest methods on the arteriovenous system. Material and methods In the study period May–September 2022, 60 patients with ischaemic heart disease, who were scheduled for surgical heart revascularization, were included. Clinical information was collected from 60 patients who met the inclusion criteria and were split into 2 groups at random. Results and conclusions The arteriovenous system of the lower extremities was unaffected by either the OVH or EVH methods utilized to harvest GSV. The OVH approach resulted in a higher rate of SSI in patients with an elevated risk of SSI based on the BHIS scale, particularly in individuals with atherosclerosis of the lower limbs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".